5 citations · 15 across the 20 of their papers we have counts for
21 papers
Beyond Confidence: Stability-Aware Test-Time Adaptation for LLM Reasoning
Bincheng Gu, Min Gao, Zongwei Wang +3
Test-time adaptation has emerged as a lightweight alternative to costly post-training for improving the reasoning capabilities of Large Language Models (LLMs) on downstream tasks.…
Personalized Communication Skills for Agentic Recommender Systems
Zongwei Wang, Min Gao, Guangyu Hu +2
Agentic recommender systems increasingly employ large language model-based UserAgents to evaluate candidate items through simulated feedback before recommendations are delivered. H…
Where Reasoning Matters: Rethinking Latent Reasoning in Semantic ID-based Generative Recommendation
Shangxin Yang, Min Gao, Zongwei Wang +1
Semantic ID-based generative recommendation predicts an item by generating a short sequence of semantic ID tokens, where each token is produced autoregressively. Latent reasoning h…
Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents
Jiayi Wu, Ruobing Xie, Zeqian Huang +6
Search agents achieve strong question-answering performance through multi-turn interactions with search engines, with Group Relative Policy Optimization (GRPO) being a widely used…
Multi-LLM Token Filtering and Routing for Sequential Recommendation
Wuhan Chen, Min Gao, Xin Xia +3
Large language models (LLMs) have recently shown promise in recommendation by providing rich semantic knowledge. While most existing approaches rely on external textual corpora to…
Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
Zongwei Wang, Min Gao, Hongzhi Yin +5
Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling itera…
DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation
Jiawei Cheng, Min Gao, Zongwei Wang +5
Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one account. Existing shared-account sequential recommendation (SSR) methods often…
Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection
Bincheng Gu, Min Gao, Junliang Yu +4
Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements…
When Agents See Humans as the Outgroup: Belief-Dependent Bias in LLM-Powered Agents
Zongwei Wang, Bincheng Gu, Hongyu Yu +5
This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal "us" versus "them" cues. When such grou…
When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation
Zongwei Wang, Min Gao, Junliang Yu +3
Graph Contrastive Learning (GCL) has demonstrated substantial promise in enhancing the robustness and generalization of recommender systems, particularly by enabling models to leve…
RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents
Zongwei Wang, Min Gao, Junliang Yu +3
The implicit feedback (e.g., clicks) in real-world recommender systems is often prone to severe noise caused by unintentional interactions, such as misclicks or curiosity-driven be…
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection
Junwei Yin, Min Gao, Kai Shu +3
The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches hav…
ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems
Zongwei Wang, Min Gao, Junliang Yu +4
Recent advances in ID-free recommender systems have attracted significant attention for effectively addressing the cold start problem. However, their vulnerability to malicious att…
Poisoning Attacks and Defenses in Recommender Systems: A Survey
Zongwei Wang, Junliang Yu, Min Gao +4
Modern recommender systems (RS) have profoundly enhanced user experience across digital platforms, yet they face significant threats from poisoning attacks. These attacks, aimed at…
Poisoning Attacks against Recommender Systems: A Survey
Zongwei Wang, Min Gao, Junliang Yu +3
Modern recommender systems (RS) have seen substantial success, yet they remain vulnerable to malicious activities, notably poisoning attacks. These attacks involve injecting malici…
Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning Attacks
Zongwei Wang, Junliang Yu, Min Gao +3
Contrastive learning (CL) has recently gained prominence in the domain of recommender systems due to its great ability to enhance recommendation accuracy and improve model robustne…
Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang, Xiaoying Zhang, Yang Liu +4
Recommendation systems aim to predict users' feedback on items not exposed to them. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic st…
Addressing the Extreme Cold-Start Problem in Group Recommendation
Guo linxin, Tao yinghui, Gao Min +3
The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems…
Efficient Bi-Level Optimization for Recommendation Denoising
Zongwei Wang, Min Gao, Wentao Li +3
The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, impli…
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation
Yinghui Tao, Min Gao, Junliang Yu +3
Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover sup…
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion
Shiqi Wang, Chongming Gao, Min Gao +3
With the increasingly fierce market competition, offering a free trial has become a potent stimuli strategy to promote products and attract users. By providing users with opportuni…